Long-range prediction of retail sales using recurrent radial basis function neural network

The literature survey on sales forecasting reveals that few works have been reported on long-range forecasting of sale volumes. On the other hand, there is a need of such long-range forecasting of sales data to devise suitable organisational strategy. The existing soft computing-based forecasting models provide poor prediction performance. Keeping this in view a new soft computing model is developed and utilised for prediction of seasonally adjusted (SA) and non-seasonally adjusted (NSA) sales volumes up to 24 months. The simulation results of real-life data show an excellent prediction performance compared to that of four other contemporary soft computing models.